Overview

Dataset statistics

Number of variables25
Number of observations35040
Missing cells20327
Missing cells (%)2.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.7 MiB
Average record size in memory200.0 B

Variable types

DateTime1
TimeSeries20
Numeric2
Categorical2

Alerts

TA_F_MDS is highly overall correlated with SW_IN_POT and 10 other fieldsHigh correlation
SW_IN_POT is highly overall correlated with TA_F_MDS and 13 other fieldsHigh correlation
SW_IN_F_MDS is highly overall correlated with TA_F_MDS and 13 other fieldsHigh correlation
LW_IN_F is highly overall correlated with SW_IN_POT and 6 other fieldsHigh correlation
LW_IN_JSB_F is highly overall correlated with TA_F_MDS and 2 other fieldsHigh correlation
VPD_ERA is highly overall correlated with TA_F_MDS and 12 other fieldsHigh correlation
VPD_F is highly overall correlated with TA_F_MDS and 13 other fieldsHigh correlation
P_F is highly overall correlated with LW_IN_FHigh correlation
USTAR is highly overall correlated with SW_IN_POT and 5 other fieldsHigh correlation
RH is highly overall correlated with TA_F_MDS and 11 other fieldsHigh correlation
NETRAD is highly overall correlated with SW_IN_POT and 11 other fieldsHigh correlation
PPFD_IN is highly overall correlated with TA_F_MDS and 13 other fieldsHigh correlation
CO2_F_MDS is highly overall correlated with TA_F_MDS and 4 other fieldsHigh correlation
TS_F_MDS_1 is highly overall correlated with TA_F_MDS and 13 other fieldsHigh correlation
LE_F_MDS is highly overall correlated with TA_F_MDS and 12 other fieldsHigh correlation
H_F_MDS is highly overall correlated with SW_IN_POT and 9 other fieldsHigh correlation
NPP_DT_VUT_MEAN is highly overall correlated with SW_IN_POT and 11 other fieldsHigh correlation
Month is highly overall correlated with DoYHigh correlation
DoY is highly overall correlated with MonthHigh correlation
NIGHT is highly overall correlated with TA_F_MDS and 10 other fieldsHigh correlation
WD has 8593 (24.5%) missing valuesMissing
USTAR has 8593 (24.5%) missing valuesMissing
RH has 1047 (3.0%) missing valuesMissing
NETRAD has 1047 (3.0%) missing valuesMissing
PPFD_IN has 1047 (3.0%) missing valuesMissing
TA_F_MDS is non stationaryNon stationary
SW_IN_POT is non stationaryNon stationary
SW_IN_F_MDS is non stationaryNon stationary
LW_IN_F is non stationaryNon stationary
LW_IN_JSB_F is non stationaryNon stationary
VPD_ERA is non stationaryNon stationary
VPD_F is non stationaryNon stationary
PA_ERA is non stationaryNon stationary
P_F is non stationaryNon stationary
WS_F is non stationaryNon stationary
RH is non stationaryNon stationary
NETRAD is non stationaryNon stationary
PPFD_IN is non stationaryNon stationary
CO2_F_MDS is non stationaryNon stationary
TS_F_MDS_1 is non stationaryNon stationary
LE_F_MDS is non stationaryNon stationary
H_F_MDS is non stationaryNon stationary
NPP_DT_VUT_MEAN is non stationaryNon stationary
Month is non stationaryNon stationary
DoY is non stationaryNon stationary
TA_F_MDS is seasonalSeasonal
SW_IN_POT is seasonalSeasonal
SW_IN_F_MDS is seasonalSeasonal
LW_IN_F is seasonalSeasonal
LW_IN_JSB_F is seasonalSeasonal
VPD_ERA is seasonalSeasonal
VPD_F is seasonalSeasonal
PA_ERA is seasonalSeasonal
P_F is seasonalSeasonal
WS_F is seasonalSeasonal
RH is seasonalSeasonal
NETRAD is seasonalSeasonal
PPFD_IN is seasonalSeasonal
CO2_F_MDS is seasonalSeasonal
TS_F_MDS_1 is seasonalSeasonal
LE_F_MDS is seasonalSeasonal
H_F_MDS is seasonalSeasonal
NPP_DT_VUT_MEAN is seasonalSeasonal
Month is seasonalSeasonal
DoY is seasonalSeasonal
Year is uniformly distributedUniform
TIMESTAMP_START has unique valuesUnique
SW_IN_POT has 16891 (48.2%) zerosZeros
SW_IN_F_MDS has 441 (1.3%) zerosZeros
VPD_ERA has 1257 (3.6%) zerosZeros
VPD_F has 12055 (34.4%) zerosZeros
P_F has 12548 (35.8%) zerosZeros
PPFD_IN has 355 (1.0%) zerosZeros

Reproduction

Analysis started2023-09-04 18:26:51.984790
Analysis finished2023-09-04 18:30:10.449994
Duration3 minutes and 18.47 seconds
Software versionpandas-profiling v3.6.6
Download configurationconfig.json

Variables

Distinct35040
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size273.9 KiB
Minimum2018-01-01 00:00:00
Maximum2019-12-31 23:30:00
2023-09-04T15:30:10.544050image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:30:10.724768image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

TA_F_MDS
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct10261
Distinct (%)29.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.687193
Minimum16.646
Maximum35.454
Zeros0
Zeros (%)0.0%
Memory size273.9 KiB
2023-09-04T15:30:10.885123image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum16.646
5-th percentile22.318
Q123.65
median24.968
Q327.517
95-th percentile30.88605
Maximum35.454
Range18.808
Interquartile range (IQR)3.867

Descriptive statistics

Standard deviation2.7061071
Coefficient of variation (CV)0.1053485
Kurtosis-0.20986186
Mean25.687193
Median Absolute Deviation (MAD)1.663
Skewness0.67347997
Sum900079.23
Variance7.3230157
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value8.626641247 Ă— 10-28
2023-09-04T15:30:11.083042image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.087 18
 
0.1%
24.397 15
 
< 0.1%
23.652 15
 
< 0.1%
24.278 15
 
< 0.1%
24.505 15
 
< 0.1%
23.607 15
 
< 0.1%
23.895 15
 
< 0.1%
24.748 15
 
< 0.1%
23.76 14
 
< 0.1%
23.247 14
 
< 0.1%
Other values (10251) 34889
99.6%
ValueCountFrequency (%)
16.646 1
< 0.1%
17.158 1
< 0.1%
17.296 1
< 0.1%
17.559 1
< 0.1%
17.794 1
< 0.1%
17.873 1
< 0.1%
18.04 1
< 0.1%
18.074 1
< 0.1%
18.08 1
< 0.1%
18.124 1
< 0.1%
ValueCountFrequency (%)
35.454 1
< 0.1%
35.437 1
< 0.1%
35.414 1
< 0.1%
35.291 1
< 0.1%
35.158 1
< 0.1%
35.104 1
< 0.1%
35.042 1
< 0.1%
34.963 1
< 0.1%
34.918 1
< 0.1%
34.714 1
< 0.1%
2023-09-04T15:30:11.449734image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ACF and PACF

SW_IN_POT
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL  ZEROS 

Distinct9280
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean419.29992
Minimum0
Maximum1396.66
Zeros16891
Zeros (%)48.2%
Memory size273.9 KiB
2023-09-04T15:30:11.754976image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median21.75625
Q3926.552
95-th percentile1320.32
Maximum1396.66
Range1396.66
Interquartile range (IQR)926.552

Descriptive statistics

Standard deviation508.63337
Coefficient of variation (CV)1.2130538
Kurtosis-1.2177985
Mean419.29992
Median Absolute Deviation (MAD)21.75625
Skewness0.67732575
Sum14692269
Variance258707.91
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.9097289582
2023-09-04T15:30:11.962996image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16891
48.2%
1324 6
 
< 0.1%
1145.47 6
 
< 0.1%
1075.6 6
 
< 0.1%
1282.16 6
 
< 0.1%
1034.67 6
 
< 0.1%
1241.87 6
 
< 0.1%
1371.77 6
 
< 0.1%
1297.14 6
 
< 0.1%
1299.28 6
 
< 0.1%
Other values (9270) 18095
51.6%
ValueCountFrequency (%)
0 16891
48.2%
0.00355701 2
 
< 0.1%
0.0248898 2
 
< 0.1%
0.0348239 2
 
< 0.1%
0.0558635 2
 
< 0.1%
0.0769754 2
 
< 0.1%
0.0831225 2
 
< 0.1%
0.0876438 2
 
< 0.1%
0.0925519 2
 
< 0.1%
0.0935894 1
 
< 0.1%
ValueCountFrequency (%)
1396.66 2
< 0.1%
1396.54 2
< 0.1%
1396.47 2
< 0.1%
1396.45 2
< 0.1%
1396.37 4
< 0.1%
1396.22 2
< 0.1%
1396.13 2
< 0.1%
1396 2
< 0.1%
1395.83 2
< 0.1%
1395.47 2
< 0.1%
2023-09-04T15:30:12.317944image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ACF and PACF

SW_IN_F_MDS
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL  ZEROS 

Distinct10445
Distinct (%)29.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean160.97425
Minimum0
Maximum1083
Zeros441
Zeros (%)1.3%
Memory size273.9 KiB
2023-09-04T15:30:12.610511image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.013
Q10.071
median4.071
Q3282.525
95-th percentile687.505
Maximum1083
Range1083
Interquartile range (IQR)282.454

Descriptive statistics

Standard deviation236.10583
Coefficient of variation (CV)1.4667304
Kurtosis0.78306831
Mean160.97425
Median Absolute Deviation (MAD)4.069
Skewness1.3857163
Sum5640537.8
Variance55745.961
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value7.525549818 Ă— 10-29
2023-09-04T15:30:12.793470image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 441
 
1.3%
0.015 391
 
1.1%
0.014 386
 
1.1%
0.013 363
 
1.0%
0.016 339
 
1.0%
0.017 328
 
0.9%
0.012 308
 
0.9%
0.018 277
 
0.8%
0.011 236
 
0.7%
0.019 233
 
0.7%
Other values (10435) 31738
90.6%
ValueCountFrequency (%)
0 441
1.3%
0.001 11
 
< 0.1%
0.002 21
 
0.1%
0.003 33
 
0.1%
0.004 26
 
0.1%
0.005 23
 
0.1%
0.006 50
 
0.1%
0.007 40
 
0.1%
0.008 72
 
0.2%
0.009 132
 
0.4%
ValueCountFrequency (%)
1083 1
< 0.1%
1053 1
< 0.1%
1052 1
< 0.1%
1024 1
< 0.1%
1016 1
< 0.1%
1012 1
< 0.1%
1007 1
< 0.1%
1001 2
< 0.1%
1000 1
< 0.1%
998 1
< 0.1%
2023-09-04T15:30:13.139050image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ACF and PACF

LW_IN_F
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct14193
Distinct (%)40.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean418.08458
Minimum334.149
Maximum453.598
Zeros0
Zeros (%)0.0%
Memory size273.9 KiB
2023-09-04T15:30:13.430097image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum334.149
5-th percentile398.77575
Q1411.0375
median418.949
Q3426.38925
95-th percentile435.2381
Maximum453.598
Range119.449
Interquartile range (IQR)15.35175

Descriptive statistics

Standard deviation11.965881
Coefficient of variation (CV)0.028620718
Kurtosis3.1291055
Mean418.08458
Median Absolute Deviation (MAD)7.671
Skewness-0.92271818
Sum14649684
Variance143.18231
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.925372736 Ă— 10-19
2023-09-04T15:30:13.604574image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
424.085 14
 
< 0.1%
405.603 10
 
< 0.1%
424.858 10
 
< 0.1%
415.09 10
 
< 0.1%
407.896 10
 
< 0.1%
415.965 8
 
< 0.1%
426.665 8
 
< 0.1%
425.794 8
 
< 0.1%
406.742 8
 
< 0.1%
427.411 8
 
< 0.1%
Other values (14183) 34946
99.7%
ValueCountFrequency (%)
334.149 2
< 0.1%
337.228 2
< 0.1%
339.054 2
< 0.1%
339.159 2
< 0.1%
342.868 2
< 0.1%
343.62 2
< 0.1%
343.847 2
< 0.1%
344.435 2
< 0.1%
345.336 2
< 0.1%
345.429 2
< 0.1%
ValueCountFrequency (%)
453.598 2
< 0.1%
452.891 2
< 0.1%
452.227 2
< 0.1%
450.738 2
< 0.1%
450.17 2
< 0.1%
450.167 2
< 0.1%
449.965 2
< 0.1%
449.692 2
< 0.1%
449.661 2
< 0.1%
449.656 2
< 0.1%
2023-09-04T15:30:13.953406image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ACF and PACF

LW_IN_JSB_F
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct23070
Distinct (%)65.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean482.93272
Minimum364.068
Maximum554.475
Zeros0
Zeros (%)0.0%
Memory size273.9 KiB
2023-09-04T15:30:14.481474image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum364.068
5-th percentile446.8559
Q1471.564
median482.6075
Q3496.1555
95-th percentile519.8324
Maximum554.475
Range190.407
Interquartile range (IQR)24.5915

Descriptive statistics

Standard deviation22.863622
Coefficient of variation (CV)0.047343286
Kurtosis2.5536884
Mean482.93272
Median Absolute Deviation (MAD)12.144
Skewness-0.72912049
Sum16921963
Variance522.74521
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.709105382 Ă— 10-22
2023-09-04T15:30:14.677277image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
470.636 13
 
< 0.1%
480.676 12
 
< 0.1%
471.799 12
 
< 0.1%
475.076 12
 
< 0.1%
472.075 12
 
< 0.1%
472.32 12
 
< 0.1%
480.842 12
 
< 0.1%
473.896 11
 
< 0.1%
473.047 11
 
< 0.1%
476.989 11
 
< 0.1%
Other values (23060) 34922
99.7%
ValueCountFrequency (%)
364.068 1
< 0.1%
366.439 1
< 0.1%
367.694 1
< 0.1%
368.266 1
< 0.1%
369.291 1
< 0.1%
369.372 1
< 0.1%
370.383 1
< 0.1%
370.448 1
< 0.1%
371.518 1
< 0.1%
371.543 1
< 0.1%
ValueCountFrequency (%)
554.475 1
< 0.1%
553.582 1
< 0.1%
553.55 1
< 0.1%
553.117 1
< 0.1%
551.867 1
< 0.1%
551.399 1
< 0.1%
551.106 1
< 0.1%
550.488 1
< 0.1%
549.997 1
< 0.1%
549.41 1
< 0.1%
2023-09-04T15:30:15.041481image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ACF and PACF

VPD_ERA
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL  ZEROS 

Distinct11022
Distinct (%)31.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7364701
Minimum0
Maximum24.373
Zeros1257
Zeros (%)3.6%
Memory size273.9 KiB
2023-09-04T15:30:15.340400image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.058
Q10.713
median2.031
Q35.377
95-th percentile12.751
Maximum24.373
Range24.373
Interquartile range (IQR)4.664

Descriptive statistics

Standard deviation4.1587233
Coefficient of variation (CV)1.1130086
Kurtosis1.9097117
Mean3.7364701
Median Absolute Deviation (MAD)1.645
Skewness1.5339199
Sum130925.91
Variance17.29498
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value6.68411299 Ă— 10-29
2023-09-04T15:30:15.511946image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1257
 
3.6%
0.788 24
 
0.1%
0.573 23
 
0.1%
0.529 22
 
0.1%
0.635 20
 
0.1%
0.503 20
 
0.1%
0.518 19
 
0.1%
0.01 19
 
0.1%
0.22 19
 
0.1%
0.231 19
 
0.1%
Other values (11012) 33598
95.9%
ValueCountFrequency (%)
0 1257
3.6%
0.001 6
 
< 0.1%
0.002 7
 
< 0.1%
0.003 7
 
< 0.1%
0.004 10
 
< 0.1%
0.005 5
 
< 0.1%
0.006 5
 
< 0.1%
0.007 9
 
< 0.1%
0.008 12
 
< 0.1%
0.009 5
 
< 0.1%
ValueCountFrequency (%)
24.373 1
< 0.1%
24.043 1
< 0.1%
23.722 1
< 0.1%
23.713 1
< 0.1%
23.688 1
< 0.1%
23.012 1
< 0.1%
23.003 1
< 0.1%
22.975 1
< 0.1%
22.939 1
< 0.1%
22.909 1
< 0.1%
2023-09-04T15:30:15.874403image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ACF and PACF

VPD_F
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL  ZEROS 

Distinct11394
Distinct (%)32.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7323385
Minimum0
Maximum29.708
Zeros12055
Zeros (%)34.4%
Memory size273.9 KiB
2023-09-04T15:30:16.163445image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.216
Q36.00925
95-th percentile14.60505
Maximum29.708
Range29.708
Interquartile range (IQR)6.00925

Descriptive statistics

Standard deviation5.1014438
Coefficient of variation (CV)1.3668224
Kurtosis2.1218842
Mean3.7323385
Median Absolute Deviation (MAD)1.216
Skewness1.5969383
Sum130781.14
Variance26.024729
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value6.557947821 Ă— 10-27
2023-09-04T15:30:16.341403image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12055
34.4%
1.083 18
 
0.1%
0.152 15
 
< 0.1%
0.42 14
 
< 0.1%
0.064 13
 
< 0.1%
0.085 12
 
< 0.1%
0.309 12
 
< 0.1%
0.226 12
 
< 0.1%
0.248 12
 
< 0.1%
0.185 12
 
< 0.1%
Other values (11384) 22865
65.3%
ValueCountFrequency (%)
0 12055
34.4%
0.001 8
 
< 0.1%
0.002 8
 
< 0.1%
0.003 6
 
< 0.1%
0.004 8
 
< 0.1%
0.005 1
 
< 0.1%
0.006 7
 
< 0.1%
0.007 8
 
< 0.1%
0.008 4
 
< 0.1%
0.009 5
 
< 0.1%
ValueCountFrequency (%)
29.708 1
< 0.1%
29.083 1
< 0.1%
29.074 1
< 0.1%
28.413 1
< 0.1%
28.209 1
< 0.1%
28.198 1
< 0.1%
27.927 1
< 0.1%
27.887 1
< 0.1%
27.858 1
< 0.1%
27.765 1
< 0.1%
2023-09-04T15:30:16.694832image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ACF and PACF

PA_ERA
Numeric time series

NON STATIONARY  SEASONAL 

Distinct1509
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.82976
Minimum98.009
Maximum99.74
Zeros0
Zeros (%)0.0%
Memory size273.9 KiB
2023-09-04T15:30:16.984630image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum98.009
5-th percentile98.402
Q198.655
median98.83
Q399.004
95-th percentile99.254
Maximum99.74
Range1.731
Interquartile range (IQR)0.349

Descriptive statistics

Standard deviation0.25881123
Coefficient of variation (CV)0.002618758
Kurtosis-0.12154421
Mean98.82976
Median Absolute Deviation (MAD)0.175
Skewness0.04961456
Sum3462994.8
Variance0.066983251
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1.736917737 Ă— 10-18
2023-09-04T15:30:17.166506image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98.892 72
 
0.2%
98.867 71
 
0.2%
98.757 70
 
0.2%
98.746 70
 
0.2%
98.727 69
 
0.2%
98.852 69
 
0.2%
98.847 68
 
0.2%
98.762 68
 
0.2%
98.788 67
 
0.2%
98.789 67
 
0.2%
Other values (1499) 34349
98.0%
ValueCountFrequency (%)
98.009 1
< 0.1%
98.022 1
< 0.1%
98.035 1
< 0.1%
98.047 1
< 0.1%
98.053 1
< 0.1%
98.055 1
< 0.1%
98.064 1
< 0.1%
98.066 2
< 0.1%
98.07 1
< 0.1%
98.071 1
< 0.1%
ValueCountFrequency (%)
99.74 1
 
< 0.1%
99.728 1
 
< 0.1%
99.723 3
< 0.1%
99.716 2
< 0.1%
99.709 1
 
< 0.1%
99.707 1
 
< 0.1%
99.706 2
< 0.1%
99.697 1
 
< 0.1%
99.689 2
< 0.1%
99.688 1
 
< 0.1%
2023-09-04T15:30:17.521572image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ACF and PACF

P_F
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL  ZEROS 

Distinct1498
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1617718
Minimum0
Maximum4.809
Zeros12548
Zeros (%)35.8%
Memory size273.9 KiB
2023-09-04T15:30:17.811082image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.01
Q30.115
95-th percentile0.9012
Maximum4.809
Range4.809
Interquartile range (IQR)0.115

Descriptive statistics

Standard deviation0.39751466
Coefficient of variation (CV)2.4572555
Kurtosis27.282971
Mean0.1617718
Median Absolute Deviation (MAD)0.01
Skewness4.5353091
Sum5668.484
Variance0.15801791
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0
2023-09-04T15:30:17.983538image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 12548
35.8%
0.001 1466
 
4.2%
0.002 650
 
1.9%
0.003 536
 
1.5%
0.004 452
 
1.3%
0.005 408
 
1.2%
0.006 356
 
1.0%
0.007 324
 
0.9%
0.008 306
 
0.9%
0.009 288
 
0.8%
Other values (1488) 17706
50.5%
ValueCountFrequency (%)
0 12548
35.8%
0.001 1466
 
4.2%
0.002 650
 
1.9%
0.003 536
 
1.5%
0.004 452
 
1.3%
0.005 408
 
1.2%
0.006 356
 
1.0%
0.007 324
 
0.9%
0.008 306
 
0.9%
0.009 288
 
0.8%
ValueCountFrequency (%)
4.809 2
< 0.1%
4.699 2
< 0.1%
4.6 2
< 0.1%
4.46 2
< 0.1%
4.457 2
< 0.1%
4.413 2
< 0.1%
4.324 2
< 0.1%
4.182 2
< 0.1%
4.092 2
< 0.1%
4.051 2
< 0.1%
2023-09-04T15:30:18.333457image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ACF and PACF

WS_F
Numeric time series

NON STATIONARY  SEASONAL 

Distinct3072
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2076588
Minimum0.008
Maximum5.673
Zeros0
Zeros (%)0.0%
Memory size273.9 KiB
2023-09-04T15:30:18.623858image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0.008
5-th percentile0.36595
Q10.76275
median1.14
Q31.568
95-th percentile2.26605
Maximum5.673
Range5.665
Interquartile range (IQR)0.80525

Descriptive statistics

Standard deviation0.60249732
Coefficient of variation (CV)0.49889699
Kurtosis1.4436392
Mean1.2076588
Median Absolute Deviation (MAD)0.4
Skewness0.84422912
Sum42316.363
Variance0.36300302
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0
2023-09-04T15:30:18.810387image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.115 38
 
0.1%
0.803 38
 
0.1%
0.833 37
 
0.1%
0.92 37
 
0.1%
0.805 36
 
0.1%
1.344 35
 
0.1%
1.016 35
 
0.1%
0.926 35
 
0.1%
0.913 35
 
0.1%
0.978 34
 
0.1%
Other values (3062) 34680
99.0%
ValueCountFrequency (%)
0.008 1
< 0.1%
0.019 2
< 0.1%
0.02 1
< 0.1%
0.021 1
< 0.1%
0.022 1
< 0.1%
0.023 2
< 0.1%
0.024 1
< 0.1%
0.025 1
< 0.1%
0.028 1
< 0.1%
0.029 1
< 0.1%
ValueCountFrequency (%)
5.673 1
< 0.1%
5.546 1
< 0.1%
4.907 1
< 0.1%
4.843 1
< 0.1%
4.842 1
< 0.1%
4.757 2
< 0.1%
4.73 1
< 0.1%
4.695 1
< 0.1%
4.685 1
< 0.1%
4.634 1
< 0.1%
2023-09-04T15:30:19.157188image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ACF and PACF

WD
Real number (ℝ)

Distinct26446
Distinct (%)> 99.9%
Missing8593
Missing (%)24.5%
Infinite0
Infinite (%)0.0%
Mean181.73394
Minimum0.008075
Maximum359.98886
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size273.9 KiB
2023-09-04T15:30:19.697120image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0.008075
5-th percentile10.96195
Q173.992747
median183.99887
Q3287.48155
95-th percentile349.09364
Maximum359.98886
Range359.98078
Interquartile range (IQR)213.48881

Descriptive statistics

Standard deviation113.44844
Coefficient of variation (CV)0.62425566
Kurtosis-1.3580073
Mean181.73394
Median Absolute Deviation (MAD)106.17482
Skewness-0.03330385
Sum4806317.4
Variance12870.548
MonotonicityNot monotonic
2023-09-04T15:30:19.867279image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
258.731408 2
 
< 0.1%
22.20081 1
 
< 0.1%
23.17391 1
 
< 0.1%
121.964815 1
 
< 0.1%
115.296494 1
 
< 0.1%
116.051932 1
 
< 0.1%
80.333305 1
 
< 0.1%
64.344576 1
 
< 0.1%
112.225019 1
 
< 0.1%
130.939517 1
 
< 0.1%
Other values (26436) 26436
75.4%
(Missing) 8593
 
24.5%
ValueCountFrequency (%)
0.008075 1
< 0.1%
0.021357 1
< 0.1%
0.021952 1
< 0.1%
0.02582 1
< 0.1%
0.036169 1
< 0.1%
0.036819 1
< 0.1%
0.050894 1
< 0.1%
0.054755 1
< 0.1%
0.063642 1
< 0.1%
0.068249 1
< 0.1%
ValueCountFrequency (%)
359.988858 1
< 0.1%
359.982808 1
< 0.1%
359.980591 1
< 0.1%
359.974032 1
< 0.1%
359.973113 1
< 0.1%
359.969801 1
< 0.1%
359.959045 1
< 0.1%
359.952244 1
< 0.1%
359.946123 1
< 0.1%
359.916635 1
< 0.1%

USTAR
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct25434
Distinct (%)96.2%
Missing8593
Missing (%)24.5%
Infinite0
Infinite (%)0.0%
Mean0.18715402
Minimum0.004563
Maximum1.279706
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size273.9 KiB
2023-09-04T15:30:20.050206image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0.004563
5-th percentile0.0477663
Q10.0943755
median0.153543
Q30.248674
95-th percentile0.4346851
Maximum1.279706
Range1.275143
Interquartile range (IQR)0.1542985

Descriptive statistics

Standard deviation0.12554928
Coefficient of variation (CV)0.67083403
Kurtosis2.7086837
Mean0.18715402
Median Absolute Deviation (MAD)0.06968
Skewness1.4165681
Sum4949.6623
Variance0.015762622
MonotonicityNot monotonic
2023-09-04T15:30:20.236556image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.098258 4
 
< 0.1%
0.15301 3
 
< 0.1%
0.126712 3
 
< 0.1%
0.146817 3
 
< 0.1%
0.102988 3
 
< 0.1%
0.121178 3
 
< 0.1%
0.211469 3
 
< 0.1%
0.140049 3
 
< 0.1%
0.107363 3
 
< 0.1%
0.068046 3
 
< 0.1%
Other values (25424) 26416
75.4%
(Missing) 8593
 
24.5%
ValueCountFrequency (%)
0.004563 1
< 0.1%
0.008137 1
< 0.1%
0.008502 1
< 0.1%
0.008564 1
< 0.1%
0.008937 1
< 0.1%
0.009629 1
< 0.1%
0.009768 1
< 0.1%
0.010033 1
< 0.1%
0.010819 1
< 0.1%
0.011025 1
< 0.1%
ValueCountFrequency (%)
1.279706 1
< 0.1%
1.113754 1
< 0.1%
1.088441 1
< 0.1%
1.009235 1
< 0.1%
0.953634 1
< 0.1%
0.934259 1
< 0.1%
0.923364 1
< 0.1%
0.913306 1
< 0.1%
0.906874 1
< 0.1%
0.905238 1
< 0.1%

RH
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY  SEASONAL 

Distinct16106
Distinct (%)47.4%
Missing1047
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean90.542143
Minimum36.811
Maximum100
Zeros0
Zeros (%)0.0%
Memory size273.9 KiB
2023-09-04T15:30:20.399366image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum36.811
5-th percentile66.3226
Q183.427
median96.189
Q3100
95-th percentile100
Maximum100
Range63.189
Interquartile range (IQR)16.573

Descriptive statistics

Standard deviation11.79846
Coefficient of variation (CV)0.13030904
Kurtosis0.67773745
Mean90.542143
Median Absolute Deviation (MAD)3.811
Skewness-1.2249859
Sum3077799.1
Variance139.20365
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value9.241554615 Ă— 10-26
2023-09-04T15:30:20.581796image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 11997
34.2%
96.481 7
 
< 0.1%
99.106 7
 
< 0.1%
98.418 7
 
< 0.1%
99.003 7
 
< 0.1%
99.727 7
 
< 0.1%
99.861 6
 
< 0.1%
98.758 6
 
< 0.1%
99.666 6
 
< 0.1%
97.266 6
 
< 0.1%
Other values (16096) 21937
62.6%
(Missing) 1047
 
3.0%
ValueCountFrequency (%)
36.811 1
< 0.1%
36.885 1
< 0.1%
37.022 1
< 0.1%
38.171 1
< 0.1%
38.223 1
< 0.1%
38.328 1
< 0.1%
38.442 1
< 0.1%
39.541 1
< 0.1%
39.792 1
< 0.1%
39.972 1
< 0.1%
ValueCountFrequency (%)
100 11997
34.2%
99.999 2
 
< 0.1%
99.998 2
 
< 0.1%
99.997 2
 
< 0.1%
99.996 2
 
< 0.1%
99.995 3
 
< 0.1%
99.994 1
 
< 0.1%
99.993 2
 
< 0.1%
99.992 5
 
< 0.1%
99.991 1
 
< 0.1%
2023-09-04T15:30:20.934448image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ACF and PACF

NETRAD
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY  SEASONAL 

Distinct29151
Distinct (%)85.8%
Missing1047
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean111.22197
Minimum-152.4
Maximum909.27
Zeros0
Zeros (%)0.0%
Memory size273.9 KiB
2023-09-04T15:30:21.225359image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-152.4
5-th percentile-35.9394
Q1-22.615
median-8.8212
Q3203.38
95-th percentile570.196
Maximum909.27
Range1061.67
Interquartile range (IQR)225.995

Descriptive statistics

Standard deviation203.94268
Coefficient of variation (CV)1.8336546
Kurtosis0.9950937
Mean111.22197
Median Absolute Deviation (MAD)24.8158
Skewness1.458291
Sum3780768.5
Variance41592.618
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value4.918101231 Ă— 10-30
2023-09-04T15:30:21.405664image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-19.033 6
 
< 0.1%
-10.964 5
 
< 0.1%
-22.832 5
 
< 0.1%
-14.07 5
 
< 0.1%
-16.722 5
 
< 0.1%
-19.09 5
 
< 0.1%
-17.621 5
 
< 0.1%
-23.687 5
 
< 0.1%
-18.917 5
 
< 0.1%
-18.059 5
 
< 0.1%
Other values (29141) 33942
96.9%
(Missing) 1047
 
3.0%
ValueCountFrequency (%)
-152.4 1
< 0.1%
-135.96 1
< 0.1%
-120.49 1
< 0.1%
-117.79 1
< 0.1%
-115.43 1
< 0.1%
-91.103 1
< 0.1%
-90.426 1
< 0.1%
-89.923 1
< 0.1%
-89.605 1
< 0.1%
-88.918 1
< 0.1%
ValueCountFrequency (%)
909.27 1
< 0.1%
897.28 1
< 0.1%
872.21 1
< 0.1%
869.89 1
< 0.1%
869.53 1
< 0.1%
859.75 1
< 0.1%
859.23 1
< 0.1%
850.16 1
< 0.1%
848.5 1
< 0.1%
847.78 1
< 0.1%
2023-09-04T15:30:21.752522image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ACF and PACF

PPFD_IN
Numeric time series

HIGH CORRELATION  MISSING  NON STATIONARY  SEASONAL  ZEROS 

Distinct9362
Distinct (%)27.5%
Missing1047
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean330.4119
Minimum0
Maximum2194
Zeros355
Zeros (%)1.0%
Memory size273.9 KiB
2023-09-04T15:30:22.043987image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.004
Q10.031
median8.55
Q3582.5
95-th percentile1396
Maximum2194
Range2194
Interquartile range (IQR)582.469

Descriptive statistics

Standard deviation482.39643
Coefficient of variation (CV)1.459985
Kurtosis0.70902161
Mean330.4119
Median Absolute Deviation (MAD)8.549
Skewness1.366303
Sum11231692
Variance232706.32
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.004399586 Ă— 10-29
2023-09-04T15:30:22.220402image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.003 523
 
1.5%
0.004 518
 
1.5%
0.005 440
 
1.3%
0.007 431
 
1.2%
0.008 413
 
1.2%
0.01 372
 
1.1%
0.006 365
 
1.0%
0.009 361
 
1.0%
0 355
 
1.0%
0.011 343
 
1.0%
Other values (9352) 29872
85.3%
(Missing) 1047
 
3.0%
ValueCountFrequency (%)
0 355
1.0%
0.001 342
1.0%
0.002 329
0.9%
0.003 523
1.5%
0.004 518
1.5%
0.005 440
1.3%
0.006 365
1.0%
0.007 431
1.2%
0.008 413
1.2%
0.009 361
1.0%
ValueCountFrequency (%)
2194 1
< 0.1%
2127 1
< 0.1%
2097 1
< 0.1%
2072 1
< 0.1%
2050 1
< 0.1%
2041 1
< 0.1%
2040 1
< 0.1%
2018 1
< 0.1%
2013 1
< 0.1%
2012 1
< 0.1%
2023-09-04T15:30:22.554806image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ACF and PACF

CO2_F_MDS
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct28581
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean434.23104
Minimum321.683
Maximum838.597
Zeros0
Zeros (%)0.0%
Memory size273.9 KiB
2023-09-04T15:30:22.845205image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum321.683
5-th percentile384.54885
Q1413.60125
median429.09
Q3453.9675
95-th percentile492.60335
Maximum838.597
Range516.914
Interquartile range (IQR)40.36625

Descriptive statistics

Standard deviation33.190443
Coefficient of variation (CV)0.076434984
Kurtosis4.5605365
Mean434.23104
Median Absolute Deviation (MAD)19.3645
Skewness1.0724992
Sum15215456
Variance1101.6055
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value9.285986853 Ă— 10-15
2023-09-04T15:30:23.019264image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
426.082 163
 
0.5%
421.601 75
 
0.2%
417.119 67
 
0.2%
418.198 58
 
0.2%
425.222 39
 
0.1%
424.864 30
 
0.1%
421.129 21
 
0.1%
420.403 18
 
0.1%
411.726 17
 
< 0.1%
457.838 16
 
< 0.1%
Other values (28571) 34536
98.6%
ValueCountFrequency (%)
321.683 1
< 0.1%
346.255 1
< 0.1%
357.237 1
< 0.1%
364.323 1
< 0.1%
365.376 1
< 0.1%
365.622 1
< 0.1%
365.98 1
< 0.1%
366.096 1
< 0.1%
366.162 1
< 0.1%
366.339 1
< 0.1%
ValueCountFrequency (%)
838.597 1
< 0.1%
811.655 1
< 0.1%
773.951 1
< 0.1%
758.623 1
< 0.1%
754.613 1
< 0.1%
744.193 1
< 0.1%
741.43 1
< 0.1%
725.891 1
< 0.1%
704.145 1
< 0.1%
702.647 1
< 0.1%
2023-09-04T15:30:23.371990image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ACF and PACF

TS_F_MDS_1
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct4529
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.218508
Minimum20.19
Maximum28.167
Zeros0
Zeros (%)0.0%
Memory size273.9 KiB
2023-09-04T15:30:23.665548image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum20.19
5-th percentile23.961
Q124.589
median25.094
Q325.836
95-th percentile26.83705
Maximum28.167
Range7.977
Interquartile range (IQR)1.247

Descriptive statistics

Standard deviation0.90980338
Coefficient of variation (CV)0.036076812
Kurtosis0.44086817
Mean25.218508
Median Absolute Deviation (MAD)0.5975
Skewness0.14770953
Sum883656.51
Variance0.82774218
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value4.014671542 Ă— 10-18
2023-09-04T15:30:23.851108image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24.863 29
 
0.1%
24.759 28
 
0.1%
24.829 28
 
0.1%
24.767 28
 
0.1%
24.822 28
 
0.1%
24.8 27
 
0.1%
24.764 27
 
0.1%
24.706 27
 
0.1%
24.824 26
 
0.1%
24.558 26
 
0.1%
Other values (4519) 34766
99.2%
ValueCountFrequency (%)
20.19 1
< 0.1%
20.231 1
< 0.1%
20.271 1
< 0.1%
20.295 1
< 0.1%
20.401 1
< 0.1%
20.468 1
< 0.1%
20.495 1
< 0.1%
20.535 1
< 0.1%
20.718 1
< 0.1%
20.732 1
< 0.1%
ValueCountFrequency (%)
28.167 1
 
< 0.1%
28.132 1
 
< 0.1%
28.121 1
 
< 0.1%
28.116 3
< 0.1%
28.097 1
 
< 0.1%
28.075 1
 
< 0.1%
28.072 1
 
< 0.1%
28.049 1
 
< 0.1%
28.028 1
 
< 0.1%
28.025 1
 
< 0.1%
2023-09-04T15:30:24.462766image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ACF and PACF

LE_F_MDS
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct30957
Distinct (%)88.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.637565
Minimum-890.726
Maximum830.368
Zeros0
Zeros (%)0.0%
Memory size273.9 KiB
2023-09-04T15:30:24.752175image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-890.726
5-th percentile-2.6288525
Q13.3942525
median11.4519
Q3143.134
95-th percentile316.453
Maximum830.368
Range1721.094
Interquartile range (IQR)139.73975

Descriptive statistics

Standard deviation113.04862
Coefficient of variation (CV)1.4195388
Kurtosis1.3842666
Mean79.637565
Median Absolute Deviation (MAD)12.930035
Skewness1.3516473
Sum2790500.3
Variance12779.99
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.312712337 Ă— 10-28
2023-09-04T15:30:24.924346image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10.1706 46
 
0.1%
55.1871 40
 
0.1%
1.78686 34
 
0.1%
110.997 32
 
0.1%
2.95633 31
 
0.1%
0.997849 30
 
0.1%
59.0961 24
 
0.1%
3.37933 21
 
0.1%
0.935616 19
 
0.1%
95.9957 19
 
0.1%
Other values (30947) 34744
99.2%
ValueCountFrequency (%)
-890.726 1
< 0.1%
-494.091 1
< 0.1%
-449.292 1
< 0.1%
-372.329 1
< 0.1%
-348.285 1
< 0.1%
-342.982 1
< 0.1%
-338.116 1
< 0.1%
-319.788 1
< 0.1%
-293.665 1
< 0.1%
-268.709 1
< 0.1%
ValueCountFrequency (%)
830.368 1
< 0.1%
718.51 1
< 0.1%
658.975 1
< 0.1%
651.613 1
< 0.1%
648.057 1
< 0.1%
639.812 1
< 0.1%
638.122 1
< 0.1%
630.574 1
< 0.1%
625.612 1
< 0.1%
624.827 1
< 0.1%
2023-09-04T15:30:25.276101image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ACF and PACF

H_F_MDS
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct31357
Distinct (%)89.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.441388
Minimum-67.5852
Maximum233.211
Zeros0
Zeros (%)0.0%
Memory size273.9 KiB
2023-09-04T15:30:25.565222image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-67.5852
5-th percentile-7.7171515
Q1-3.2009475
median-1.019835
Q321.7344
95-th percentile85.82377
Maximum233.211
Range300.7962
Interquartile range (IQR)24.935347

Descriptive statistics

Standard deviation31.409595
Coefficient of variation (CV)2.1749707
Kurtosis3.7858816
Mean14.441388
Median Absolute Deviation (MAD)3.643925
Skewness1.9649931
Sum506026.23
Variance986.56266
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.367832088 Ă— 10-29
2023-09-04T15:30:25.748827image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.10108 34
 
0.1%
2.02778 34
 
0.1%
-12.1429 32
 
0.1%
-3.91578 31
 
0.1%
-1.95131 27
 
0.1%
-7.68574 24
 
0.1%
-5.96818 22
 
0.1%
-3.88611 21
 
0.1%
98.1487 21
 
0.1%
14.4101 19
 
0.1%
Other values (31347) 34775
99.2%
ValueCountFrequency (%)
-67.5852 1
< 0.1%
-62.0882 1
< 0.1%
-59.145 1
< 0.1%
-58.2684 1
< 0.1%
-57.1396 1
< 0.1%
-53.2815 1
< 0.1%
-53.2095 1
< 0.1%
-52.1135 1
< 0.1%
-50.7903 1
< 0.1%
-49.3939 1
< 0.1%
ValueCountFrequency (%)
233.211 1
< 0.1%
206.889 2
< 0.1%
204.781 1
< 0.1%
203.388 1
< 0.1%
198.956 2
< 0.1%
195.864 1
< 0.1%
194.524 1
< 0.1%
194.379 1
< 0.1%
189.244 1
< 0.1%
185.809 1
< 0.1%
2023-09-04T15:30:26.104937image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ACF and PACF

NIGHT
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size273.9 KiB
0
18149 
1
16891 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters35040
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
0 18149
51.8%
1 16891
48.2%

Length

2023-09-04T15:30:26.395068image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-04T15:30:26.549889image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
0 18149
51.8%
1 16891
48.2%

Most occurring characters

ValueCountFrequency (%)
0 18149
51.8%
1 16891
48.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 35040
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 18149
51.8%
1 16891
48.2%

Most occurring scripts

ValueCountFrequency (%)
Common 35040
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 18149
51.8%
1 16891
48.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35040
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 18149
51.8%
1 16891
48.2%

NPP_DT_VUT_MEAN
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct34996
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.9156691
Minimum-14.983349
Maximum44.38187
Zeros0
Zeros (%)0.0%
Memory size273.9 KiB
2023-09-04T15:30:26.666460image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum-14.983349
5-th percentile-10.346075
Q1-7.5224222
median-4.9035614
Q310.141305
95-th percentile21.792095
Maximum44.38187
Range59.365219
Interquartile range (IQR)17.663727

Descriptive statistics

Standard deviation11.029131
Coefficient of variation (CV)12.044888
Kurtosis-0.55408224
Mean0.9156691
Median Absolute Deviation (MAD)4.464552
Skewness0.84680023
Sum32085.045
Variance121.64174
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value3.337068955 Ă— 10-25
2023-09-04T15:30:26.841682image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.7545 2
 
< 0.1%
10.3963 2
 
< 0.1%
23.8708 2
 
< 0.1%
6.27842 2
 
< 0.1%
15.2212 2
 
< 0.1%
18.67425 2
 
< 0.1%
-5.53233 2
 
< 0.1%
12.846 2
 
< 0.1%
-4.3723 2
 
< 0.1%
6.0224 2
 
< 0.1%
Other values (34986) 35020
99.9%
ValueCountFrequency (%)
-14.98334882 1
< 0.1%
-14.86877267 1
< 0.1%
-14.8506271 1
< 0.1%
-14.84780343 1
< 0.1%
-14.81002648 1
< 0.1%
-14.80620371 1
< 0.1%
-14.80299821 1
< 0.1%
-14.78890102 1
< 0.1%
-14.77209508 1
< 0.1%
-14.76874927 1
< 0.1%
ValueCountFrequency (%)
44.38187 1
< 0.1%
40.8644 1
< 0.1%
39.52697 1
< 0.1%
38.69425 1
< 0.1%
38.34727 1
< 0.1%
37.7322 1
< 0.1%
37.4525 1
< 0.1%
37.435 1
< 0.1%
37.4206 1
< 0.1%
37.1546 1
< 0.1%
2023-09-04T15:30:27.186865image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ACF and PACF

Year
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size273.9 KiB
2018
17520 
2019
17520 

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters140160
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2018 17520
50.0%
2019 17520
50.0%

Length

2023-09-04T15:30:27.478604image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-09-04T15:30:27.620930image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ValueCountFrequency (%)
2018 17520
50.0%
2019 17520
50.0%

Most occurring characters

ValueCountFrequency (%)
2 35040
25.0%
0 35040
25.0%
1 35040
25.0%
8 17520
12.5%
9 17520
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 140160
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 35040
25.0%
0 35040
25.0%
1 35040
25.0%
8 17520
12.5%
9 17520
12.5%

Most occurring scripts

ValueCountFrequency (%)
Common 140160
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 35040
25.0%
0 35040
25.0%
1 35040
25.0%
8 17520
12.5%
9 17520
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 140160
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 35040
25.0%
0 35040
25.0%
1 35040
25.0%
8 17520
12.5%
9 17520
12.5%

Month
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct12
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.5260274
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Memory size273.9 KiB
2023-09-04T15:30:27.740835image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.4479005
Coefficient of variation (CV)0.52833068
Kurtosis-1.207053
Mean6.5260274
Median Absolute Deviation (MAD)3
Skewness-0.010456852
Sum228672
Variance11.888018
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.4100461101
2023-09-04T15:30:27.890419image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 2976
8.5%
3 2976
8.5%
5 2976
8.5%
7 2976
8.5%
8 2976
8.5%
10 2976
8.5%
12 2976
8.5%
4 2880
8.2%
6 2880
8.2%
9 2880
8.2%
Other values (2) 5568
15.9%
ValueCountFrequency (%)
1 2976
8.5%
2 2688
7.7%
3 2976
8.5%
4 2880
8.2%
5 2976
8.5%
6 2880
8.2%
7 2976
8.5%
8 2976
8.5%
9 2880
8.2%
10 2976
8.5%
ValueCountFrequency (%)
12 2976
8.5%
11 2880
8.2%
10 2976
8.5%
9 2880
8.2%
8 2976
8.5%
7 2976
8.5%
6 2880
8.2%
5 2976
8.5%
4 2880
8.2%
3 2976
8.5%
2023-09-04T15:30:28.226661image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ACF and PACF

DoY
Numeric time series

HIGH CORRELATION  NON STATIONARY  SEASONAL 

Distinct365
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean183
Minimum1
Maximum365
Zeros0
Zeros (%)0.0%
Memory size273.9 KiB
2023-09-04T15:30:28.522292image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19
Q192
median183
Q3274
95-th percentile347
Maximum365
Range364
Interquartile range (IQR)182

Descriptive statistics

Standard deviation105.36753
Coefficient of variation (CV)0.57577886
Kurtosis-1.200018
Mean183
Median Absolute Deviation (MAD)91
Skewness0
Sum6412320
Variance11102.317
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.4146540776
2023-09-04T15:30:28.690589image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 96
 
0.3%
252 96
 
0.3%
250 96
 
0.3%
249 96
 
0.3%
248 96
 
0.3%
247 96
 
0.3%
246 96
 
0.3%
245 96
 
0.3%
244 96
 
0.3%
243 96
 
0.3%
Other values (355) 34080
97.3%
ValueCountFrequency (%)
1 96
0.3%
2 96
0.3%
3 96
0.3%
4 96
0.3%
5 96
0.3%
6 96
0.3%
7 96
0.3%
8 96
0.3%
9 96
0.3%
10 96
0.3%
ValueCountFrequency (%)
365 96
0.3%
364 96
0.3%
363 96
0.3%
362 96
0.3%
361 96
0.3%
360 96
0.3%
359 96
0.3%
358 96
0.3%
357 96
0.3%
356 96
0.3%
2023-09-04T15:30:29.087643image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
ACF and PACF

Interactions

2023-09-04T15:30:05.720279image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:28:54.010262image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:28:57.508933image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:00.831417image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:04.269650image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:07.483361image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:11.069127image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:14.317168image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:17.842498image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:21.230215image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:24.619096image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:27.805703image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:31.455383image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:35.127689image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:38.567733image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:42.131623image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:45.405870image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:48.840715image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:52.280701image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:55.801636image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:59.058604image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:30:02.516273image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:30:05.876268image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:28:54.173078image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:28:57.663335image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:00.981055image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:04.418252image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:07.644482image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:11.219449image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:14.471705image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:18.004568image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:21.378386image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:24.772510image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:27.972741image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:31.614117image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:35.290468image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:38.727679image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:42.289648image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:45.558077image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:49.002537image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:52.435407image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:55.953199image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:59.212635image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:30:02.665927image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:30:06.030986image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:28:54.327022image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:28:57.817004image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:01.126897image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:04.563114image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:07.800812image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:11.369794image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:14.651593image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:18.162736image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:21.526706image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:24.920311image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:28.129690image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
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2023-09-04T15:29:48.553932image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:51.974648image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:55.506367image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:58.767981image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:30:01.994875image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:30:05.434021image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:30:08.860399image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:28:57.354443image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:00.679595image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:04.114635image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:07.335686image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:10.913663image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:14.163382image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:17.692098image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:21.073076image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:24.471987image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:27.653372image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:31.071011image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:34.958729image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:38.410028image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:41.978277image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:45.257618image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:48.690742image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:52.122218image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:55.649519image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:29:58.904663image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:30:02.367279image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
2023-09-04T15:30:05.570691image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/

Correlations

2023-09-04T15:30:29.401440image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
TA_F_MDSSW_IN_POTSW_IN_F_MDSLW_IN_FLW_IN_JSB_FVPD_ERAVPD_FPA_ERAP_FWS_FWDUSTARRHNETRADPPFD_INCO2_F_MDSTS_F_MDS_1LE_F_MDSH_F_MDSNPP_DT_VUT_MEANMonthDoYNIGHTYear
TA_F_MDS1.0000.5520.5060.3950.6860.7800.872-0.2940.2040.007-0.2040.333-0.8720.4740.548-0.5880.8250.5720.4840.486-0.024-0.0230.5040.044
SW_IN_POT0.5521.0000.9100.5910.3030.6690.6260.1400.4550.007-0.1880.550-0.6250.8680.911-0.4140.6250.8140.7350.864-0.003-0.0030.9030.000
SW_IN_F_MDS0.5060.9101.0000.5340.1850.6030.5740.1350.418-0.004-0.1620.527-0.5740.8720.873-0.4150.5710.7920.7240.8500.0250.0250.7410.044
LW_IN_F0.3950.5910.5341.0000.3150.3820.344-0.1280.5370.028-0.1180.373-0.3340.5430.520-0.2700.5660.4760.4280.483-0.040-0.0360.5020.117
LW_IN_JSB_F0.6860.3030.1850.3151.0000.4900.512-0.3200.1580.016-0.1290.147-0.4980.1780.231-0.3720.5950.2700.2010.216-0.066-0.0670.3330.061
VPD_ERA0.7800.6690.6030.3820.4901.0000.819-0.1490.2190.072-0.1890.422-0.8170.5490.620-0.6100.8070.6380.5200.5560.0040.0020.5750.027
VPD_F0.8720.6260.5740.3440.5120.8191.000-0.1260.1930.032-0.1900.380-0.9990.5370.623-0.6350.7210.6550.5410.563-0.010-0.0100.5340.039
PA_ERA-0.2940.1400.135-0.128-0.320-0.149-0.1261.0000.076-0.0630.0560.0490.1160.1410.0850.182-0.3440.0740.1440.199-0.034-0.0340.2130.064
P_F0.2040.4550.4180.5370.1580.2190.1930.0761.0000.033-0.0700.320-0.1860.4500.383-0.1940.3610.3660.3400.392-0.056-0.0530.1880.024
WS_F0.0070.007-0.0040.0280.0160.0720.032-0.0630.0331.0000.1730.309-0.034-0.020-0.013-0.1180.0590.045-0.073-0.009-0.000-0.0000.0990.048
WD-0.204-0.188-0.162-0.118-0.129-0.189-0.1900.056-0.0700.1731.000-0.0340.188-0.178-0.1800.082-0.197-0.192-0.148-0.1500.0400.0430.0000.006
USTAR0.3330.5500.5270.3730.1470.4220.3800.0490.3200.309-0.0341.000-0.3780.5400.521-0.3190.4260.5820.3670.510-0.015-0.0140.0800.000
RH-0.872-0.625-0.574-0.334-0.498-0.817-0.9990.116-0.186-0.0340.188-0.3781.000-0.535-0.6220.637-0.710-0.654-0.541-0.5620.0120.0120.3630.125
NETRAD0.4740.8680.8720.5430.1780.5490.5370.1410.450-0.020-0.1780.540-0.5351.0000.845-0.3300.5430.7910.7240.822-0.018-0.0170.3220.000
PPFD_IN0.5480.9110.8730.5200.2310.6200.6230.0850.383-0.013-0.1800.521-0.6220.8451.000-0.3750.5850.7920.7150.8170.0950.0960.8470.265
CO2_F_MDS-0.588-0.414-0.415-0.270-0.372-0.610-0.6350.182-0.194-0.1180.082-0.3190.637-0.330-0.3751.000-0.554-0.458-0.318-0.342-0.178-0.1800.3740.132
TS_F_MDS_10.8250.6250.5710.5660.5950.8070.721-0.3440.3610.059-0.1970.426-0.7100.5430.585-0.5541.0000.5860.4740.502-0.002-0.0010.5540.086
LE_F_MDS0.5720.8140.7920.4760.2700.6380.6550.0740.3660.045-0.1920.582-0.6540.7910.792-0.4580.5861.0000.6070.773-0.008-0.0080.5550.011
H_F_MDS0.4840.7350.7240.4280.2010.5200.5410.1440.340-0.073-0.1480.367-0.5410.7240.715-0.3180.4740.6071.0000.714-0.036-0.0340.5480.052
NPP_DT_VUT_MEAN0.4860.8640.8500.4830.2160.5560.5630.1990.392-0.009-0.1500.510-0.5620.8220.817-0.3420.5020.7730.7141.000-0.071-0.0700.8440.057
Month-0.024-0.0030.025-0.040-0.0660.004-0.010-0.034-0.056-0.0000.040-0.0150.012-0.0180.095-0.178-0.002-0.008-0.036-0.0711.0000.9970.0070.000
DoY-0.023-0.0030.025-0.036-0.0670.002-0.010-0.034-0.053-0.0000.043-0.0140.012-0.0170.096-0.180-0.001-0.008-0.034-0.0700.9971.0000.0070.000
NIGHT0.5040.9030.7410.5020.3330.5750.5340.2130.1880.0990.0000.0800.3630.3220.8470.3740.5540.5550.5480.8440.0070.0071.0000.000
Year0.0440.0000.0440.1170.0610.0270.0390.0640.0240.0480.0060.0000.1250.0000.2650.1320.0860.0110.0520.0570.0000.0000.0001.000

Missing values

2023-09-04T15:30:09.360482image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
A simple visualization of nullity by column.
2023-09-04T15:30:09.886014image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-09-04T15:30:10.272817image/svg+xmlMatplotlib v3.6.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

TIMESTAMP_STARTTA_F_MDSSW_IN_POTSW_IN_F_MDSLW_IN_FLW_IN_JSB_FVPD_ERAVPD_FPA_ERAP_FWS_FWDUSTARRHNETRADPPFD_INCO2_F_MDSTS_F_MDS_1LE_F_MDSH_F_MDSNIGHTNPP_DT_VUT_MEANYearMonthDoY
02018-01-01 00:00:0024.1960.00.000411.887479.3381.1051.13698.8300.0481.091<NA><NA><NA><NA><NA>443.97924.4747.21952-4.533601-8.224040201811
12018-01-01 00:30:0024.1950.00.000411.887479.3480.8891.13098.7930.0480.952<NA><NA><NA><NA><NA>443.97924.4107.21952-4.476881-8.223680201811
22018-01-01 01:00:0023.1800.00.082415.333471.4560.6720.02498.7560.1041.459245.8492740.08440999.914-21.6950.014454.89924.3459.34216-2.809191-7.855525201811
32018-01-01 01:30:0022.7390.00.082415.333466.8730.6300.06498.7250.1041.830272.0328120.25149499.768-19.8870.014451.78424.2816.729595.669481-7.705405201811
42018-01-01 02:00:0022.5370.00.085411.126464.9850.5880.00098.6950.0681.438275.2975690.208431100.0-17.9550.013451.78424.2416.729594.185611-7.636796201811
52018-01-01 02:30:0022.5150.00.083411.126464.7620.4550.00098.6840.0680.951309.5080080.148144100.0-15.9020.014451.78424.2026.729592.952871-7.629775201811
62018-01-01 03:00:0022.5000.00.083409.370464.5680.3210.01798.6730.0141.290308.9192810.13575399.936-20.2260.014451.78424.2526.72959-4.086731-7.624735201811
72018-01-01 03:30:0022.5040.00.082409.370464.1690.3230.19898.6600.0140.965334.8302730.06593699.275-23.5980.013451.78424.3026.72959-4.086731-7.626255201811
82018-01-01 04:00:0022.7280.00.080416.003466.8170.3250.04198.6470.0000.8772.3830670.0575799.851-17.0880.016451.78424.5246.72959-3.867341-7.702044201811
92018-01-01 04:30:0022.6670.00.080416.003466.2991.0490.00098.6780.0000.55641.4993170.086065100.0-26.140.014451.78424.7476.72959-3.893661-7.681454201811
TIMESTAMP_STARTTA_F_MDSSW_IN_POTSW_IN_F_MDSLW_IN_FLW_IN_JSB_FVPD_ERAVPD_FPA_ERAP_FWS_FWDUSTARRHNETRADPPFD_INCO2_F_MDSTS_F_MDS_1LE_F_MDSH_F_MDSNIGHTNPP_DT_VUT_MEANYearMonthDoY
350302019-12-31 19:00:0028.7470.00.046421.850475.7055.8676.53298.4230.0001.81387.0227720.05028683.458-25.4190.01379.11825.883-10.7472002.809831-11.271773201912365
350312019-12-31 19:30:0028.0270.00.057421.850472.0495.4424.56598.4890.0001.49785.5611120.14554487.944-25.6620.01385.20925.838-18.2109008.703771-10.907352201912365
350322019-12-31 20:00:0027.8460.00.056422.525469.0995.0175.07798.5540.0531.564108.9945650.06916186.451-15.7170.009383.53525.728-2.664750-0.159181-10.817372201912365
350332019-12-31 20:30:0027.4980.00.054422.525467.7344.5033.96998.6160.0533.01178.2945180.17440689.191-53.6870.01398.96625.619-2.530540-3.911311-10.645512201912365
350342019-12-31 21:00:0026.6640.00.060420.736463.8233.9891.67898.6770.0212.37678.4518540.21833595.202-24.8230.011409.78025.68416.530700-9.188971-10.239091201912365
350352019-12-31 21:30:0026.2720.00.059420.736458.5333.8242.34498.7030.0212.02218.5486740.29138393.141-18.6250.042401.85725.7495.916690-3.611201-10.051711201912365
350362019-12-31 22:00:0025.4820.00.057424.043452.2383.6591.58198.7280.1751.23812.2198580.12648795.151-24.1620.012389.17525.6406.288130-3.640641-9.680612201912365
350372019-12-31 22:30:0025.1680.00.055424.043450.6503.4300.86098.7260.1750.7592.8319470.16085397.314-17.3870.012390.32625.5316.421760-3.691841-9.535602201912365
350382019-12-31 23:00:0024.9000.00.062420.047448.5743.2000.60398.7230.0540.47816.5291040.12498398.087-17.120.018397.24225.4536.451640-3.475101-9.411851201912365
350392019-12-31 23:30:0024.6600.00.084420.047446.7052.8370.38498.6990.0540.685192.4644370.12148498.762-20.4520.012398.45025.3750.971293-4.735201-9.299978201912365